mixKernel: Omics Data Integration Using Kernel Methods

Kernel-based methods are powerful methods for integrating
heterogeneous types of data. mixKernel aims at providing methods to combine
kernel for unsupervised exploratory analysis. Different solutions are
provided to compute a meta-kernel, in a consensus way or in a way that
best preserves the original topology of the data. mixKernel also integrates
kernel PCA to visualize similarities between samples in a non linear space
and from the multiple source point of view. Functions to assess and display
important variables are also provided in the package. Jerome Mariette and
Nathalie Villa-Vialaneix (2017) <doi:10.1093/bioinformatics/btx682>.